The present invention relates to detection and classification of sonar targets. More specifically, but without limitation thereto, the present invention relates to extracting and representing target and clutter information about stationary and moving objects from radar and sonar echo data.
Previous methods for processing sonar data use coherent processing and compensate for delay perturbations by correlating echoes or images obtained from different aspect angles and/or by adaptive focusing on relatively large point reflectors (reference points).
Coherent processing complicates the receiver and makes some of the feature images more difficult to obtain. For broadband signals, the envelope of a matched filter (or sharpened matched filter) is sufficiently narrow that only a few cycles of the carrier frequency are contained within the envelope. The resolution lost by using noncoherent processing (envelope detected sharpened matched filter outputs) instead of coherent processing (phase sensitive representation of the sharpened matched filter outputs) is minimal for signals with bandwidths that are nearly as large as their center frequencies.
Detection of weakly reflecting objects that are immersed in highly cluttered environments (e.g. buried in sand) using conventional images is almost impossible. A conventional image represents target reflectivity as a function of position on the image plane. To solve this problem, images other than a conventional representation of target reflectivity are required.
A conventional image becomes defocused by unpredictable motion because of the introduction of delay errors. Intuitively, it seems reasonable to correct for delay errors by using a correction that either yields high correlation between a new echo or image and previous images or maintains a sharp image of a relatively large reflector selected as a reference point. These approaches, although widely used, can be shown to be suboptimal by a simple example.
In the synthetic aperture array of the prior art shown in FIGS. 1 and 2, an image of a point 102 on a rotating target 104 is obtained by summing echo samples arriving at sensor 106 that contain point 102 as target 104 rotates. The propagation delay over distance d must be compensated to add the appropriate echo sample to corresponding samples from other aspect angles in the predicted rotation of the actual target.
FIG. 2 shows the equivalent of FIG. 1 wherein a rotating sensor 206 rotates in an arc relative to stationary target 204. The sequence of equivalent sensor positions defines a synthetic aperture array. To sum the echo samples from point 202, the array is focused on point 202 by delay-and-sum beam forming. Intersecting line segments 208 represent different constant range surfaces in the far field of sensor 206. The length of each line segment 208 represents the beam width of the synthetic aperture array at the target location. At succeeding rotation angles, the largest image point is kept focused by rotating the target so that the same point is always closest to the sensor. The resulting image will be a line through the largest point rather than a circle (or a cylinder for a three dimensional system). This erroneous image will also have maximum correlation from echo to echo. The true image is formed without rotation "corrections". In this image, each point is represented by an asterisk as shown in FIG. 2, where the center point of the asterisk becomes much larger than the individual lines as the number of observation angles n becomes large. In this case, the ratio of the amplitude of the asterisk's central peak to the amplitude of each line equals n. Each image point is "smeared" by an asterisk-shaped point spread function with peak-to-sidelobe ratio equal to n. The point spread function becomes more and more like an impulse or point as the number of observations increases. The ideal point spread function is an impulse or point, since in this case the image is minimally smeared or defocused. A non-ideal point spread function results in a smeared or defocused image. Defocusing obscures information that would otherwise be available to a human or computational observer.
A need therefore exists for a better signal processing technique for representing features that are extracted from sonar data as images and for adaptive focusing of such images.